An Evaluation and Comparison of Current Peer-to-Peer Full-Text Keyword Search Techniques Ming Zhong, Justin Moore, Kai Shen Amy L. Murphy Department of Computer Science School of Informatics University of Rochester, USA University of Lugano, Switzerland Current peer-to-peer (p2p) full-text keyword search techniques fall into the following categories: document-based partitioning, keyword-based partitioning, hybrid indexing, and semantic search. This paper provides a performance evaluation and comparison of these p2p full-text keyword search techniques on a dataset with 3.7 million web pages and 6.8 million search queries. Our evaluation results can serve as a guide for choosing the most suitable p2p full-text keyword search technique based on given system parameters, such as network size, the number of documents, and the number of queries per second.